Using temporal filters for automated real-time classification

ABSTRACT

In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 16/907,125, filed Jun.19, 2020, and entitled USING TEMPORAL FILTERS FOR AUTOMATED REAL-TIMECLASSIFICATION, the entirety of which is hereby incorporated byreference.

BACKGROUND

Multiclass classifiers are used to assign a class distribution to aninput signal. The class distribution may include a confidence scoreindicating that the input signal should be assigned to one or more ofthe classes. For example, multiclass classifiers may be used to classifya temporal sequence of input signals, where each signal in the sequencemay be assigned a corresponding class distribution.

The most common approach in existing solutions is to run theclassification network at a constant signal analysis rate (e.g., windowsize). However, this constant analysis rate approach may sufferdecreased accuracy during class transitions. For example,mid-transition, the signals being analyzed may include half representinga first class and half representing a second class. As such, existingtechnologies fail to adapt the classification process to account forpossible class transitions.

Currently, despite best efforts at training, classifiers willincorrectly classify some input signals into an improper class. Thisperformance can be measured in using confusion analysis. For example,the current approach is to retrain classifiers until the measuredconfusion is deemed acceptable for deployment in the particular usecase; however, this approach does not account for the confusion whencalculating the final classification result. Instead, the typicalapproach is to use the class assigned the highest confidence scorewithout other considerations.

SUMMARY

Embodiments of the present disclosure relate to using temporal filtersfor automated real-time classification. Systems and methods aredisclosed for improving the performance of a multiclass classifier thatmay be used to classify a temporal sequence of input signals—such asinput signals representative of video frames. A performance improvementof the system may be achieved, at least in part, by applying thetemporal filter to an output of the multiclass classifier. The temporalfilters described herein may correspond to, without limitation, a linearfilter, a nonlinear filter, an adaptive filter, and/or a statisticalfilter. As an example, a temporal filter may leverage classificationsassociated with preceding input signals to improve the finalclassification given to a subsequent signal.

In contrast to conventional systems, such as those described above, thetechnology described herein may leverage classifications associated withpreceding input signals to improve the final classification given to asubsequent signal, while also factoring in a confusion matrix to correctfor the probable occurrence of certain types of classification errors.In some embodiments, a preliminary signal analysis may detect apresumptive class change in the classifier output, for example, asevidenced by the highest confidence score in the raw outputtransitioning from association with a first class to a second class. Aclass shift may indicate that older output data may be less relevantthan the newer output data and this information may be taken intoaccount by the adaptive filter by giving more weight to recentclassification outputs when the preliminary signal analysis detects aclass shift.

In some embodiments, a normalization process may adjust the rawclassification confidence scores according to data from a confusionmatrix. In general, the confidence score assigned to a first class(e.g., class A) may be lowered in proportion to the probability that thefirst class is a false positive of the other classes (e.g., class B,class C, class D). Conversely, the confidence score for a given classmay be increased in proportion to the probabilities that other classesare false positives for the given class. The normalization process mayoptimize or improve the accuracy of the classification by accounting forthe probability of different kinds of errors occurring in theclassification. As a result, when the normalization process is combinedwith the temporal filtering operation—which uses data from multipleconsecutive classifications—the overall classification accuracy of thesystem may be meaningfully improved without a significant contributionto the overall latency of the classification pipeline.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for using temporal filters for automatedreal-time classification are described in detail below with reference tothe attached drawing figures, wherein:

FIG. 1 is an illustration of an example real-time signal classificationsystem, in accordance with some embodiments of the present disclosure;

FIG. 2 is an illustration of an example softmax output and an angularhardness output, in accordance with some embodiments of the presentdisclosure;

FIG. 3 is an illustration of an example classification change inresponse to a signal change, in accordance with some embodiments of thepresent disclosure;

FIG. 4 is an illustration of an example confusion matrix, in accordancewith some embodiments of the present disclosure;

FIGS. 5-7 are flow charts showing methods of assigning a classificationto an input signal, in accordance with some embodiments of the presentdisclosure;

FIG. 8A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 8B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 8A, in accordance with someembodiments of the present disclosure;

FIG. 8C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 8A, in accordance with someembodiments of the present disclosure;

FIG. 8D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 8A, in accordancewith some embodiments of the present disclosure; and

FIG. 9 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to using temporal filters forautomated real-time classification. The technology described hereinimproves the performance of a multiclass classifier that may be used toclassify a temporal sequence of input signals—such as input signalsrepresentative of video frames. A performance improvement of the systemmay be achieved, at least in part, by applying the temporal filter to anoutput of the multiclass classifier. For example, the temporal filtermay leverage classifications associated with preceding input signals toimprove the final classification given to a subsequent signal. In someembodiments, the temporal filter may also use data from a confusionmatrix to correct for the probable occurrence of certain types ofclassification errors.

Depending on the embodiment, the temporal filter may take many differentforms. For example, the temporal filter may be a linear filter, anonlinear filter, an adaptive filter, and/or a statistical filter. Ineach example, the overall operation of the filter may be similar. Forexample, the filter may receive a temporal sequence of outputs from themulticlass classifier—e.g., x number of consecutive outputs generated byclassifying x number of consecutive input signals. In embodiments, thenumber of outputs received may be described as an analysis window. Asthe outputs are received, the outputs may be filtered together and afinal confidence score for each class in each instance of the outputdata may be generated using the temporal filter.

Each individual output in the sequence may include a series ofconfidence scores for each class the multiclass classifier is trained toidentify. For example, a classifier trained to assign one of fivedifferent classes to an input signal would output a confidence score foreach of the five classes. As described herein, the temporal filter mayreceive, as input, a sequence of outputs of the multiclass classifierand generate a final confidence factor for each class. The finalconfidence factor may correspond to the final output of the process andeffectively replace the newest raw output within the sequence of outputsinput to the temporal filter. The final output may then be used toassign an active classification to the corresponding input signal, andthis process may repeat as new outputs are received from theclassifier—with the oldest output dropping out of the sequence and thenewest one being added (e.g., as a rolling buffer of output signals).

Aspects of the technology described herein may account for confusionbetween classes within the temporal filter by applying a classnormalization to the raw output data using data from a confusion matrix.For example, the class confusion may be determined by analyzing theperformance of the trained classifier using ground truth data. Theground truth data may be determined, as a non-limiting example, byhaving a user assign a ground truth label to the signal input used totest the classifier performance. In some embodiments, the classconfusion analysis may be an off-line process that results in a classconfusion matrix or other memorialization of the confusion analysis.However, in other embodiments, the class confusion analysis may be on anon-line process, a process that occurs at initialization of the system,and/or at another time.

Data from the confusion matrix may be used in a normalization process.For example, because class confusion may assign a probability ofoccurrence to certain types of classification failures, then, for agiven class, the confusion matrix may include data indicating aprobability that an input signal with a ground truth classification inthe given class is a true positive or a false positive classification. Atrue positive may indicate that the input signal was correctlyclassified into the given class and a false positive may indicate thatan input signal was incorrectly classified into a different class. Eachdifferent class may receive its own probability of receiving a falsepositive classification for the given class.

In some embodiments, the normalization process may adjust the rawclassification confidence scores according to data from the confusionmatrix. In general, the confidence score assigned to a first class(e.g., class A) may be lowered in proportion to the probability that thefirst class is a false positive of the other classes (e.g., class B,class C, class D). Conversely, the confidence score for a given classmay be increased in proportion to the probabilities that other classesare false positives for the given class. The normalization process mayoptimize or improve the accuracy of the classification by accounting forthe probability of different kinds of errors occurring in theclassification. As a result, when the normalization process is combinedwith the temporal filtering operation—which uses data from multipleconsecutive classifications—the overall classification accuracy of thesystem may be meaningfully improved without a significant contributionto the overall latency of the classification pipeline.

As mentioned, the temporal filter may be a linear filter, a nonlinearfilter, an adaptive filter, and/or a statistical filter. Where anadaptive filter is implemented, the adaptive filter may use apreliminary signal analysis to change features of the function usedwithin the temporal filter. The preliminary signal analysis may be, inembodiments, executed over a smaller output window than is used by thetemporal filter. For a non-limiting example, the preliminary signalanalysis may be over five consecutive outputs, whereas a default windowfor the temporal filter may be twenty or more consecutive outputs. Insome embodiments, the preliminary signal analysis may detect apresumptive class change in the classifier output, for example, asevidenced by the highest confidence score in the raw outputtransitioning from association with a first class to a second class.This may indicate a classification shift from the first class to thesecond class.

A class shift may indicate that older output data may be less relevantthan the newer output data. This information may be taken into accountby the adaptive filter by giving more weight to recent classificationoutputs when the preliminary signal analysis detects a class shift. Thechange to the weighting values may be applied to all classes or to justaffected classes. For example, a presumptive class shift between thefirst class and the second class may cause the adaptive filter to adjusta decay function within the adaptive filter to give less weight to olderoutputs being considered by the filter that correspond to the firstclass, while leaving the default weights in place for the other classes.

Aspects of the technology described herein may work with a variety ofdifferent multiclass classifiers, but will most often be describedherein in the context of convolutional neural networks (CNNs). In someaspects, the multiclass classifier described herein may not considerclassifications assigned to preceding input signals when generating aclassification for a subsequent signal in a temporal sequence. Thetechnology described herein can serve as an alternative to a recurrentneural network (RNN), such as Long Short Term Memory (LSTM) networks,and other classifiers that already consider preceding classificationdata when calculating a subsequent classification. The use of thetemporal filter on the output of a CNN consumes less computer resourcesand contributes less to latency than using an RNN—thereby decreasingrun-time of the system—while achieving performance improvements. Thetemporal filter also allows for application specific classificationtuning that is not possible with an RNN. For example, different filterparameters may be used on different class confidence scores whereavoiding a false positive for some classes is more important than forother classes.

With reference to FIG. 1 , FIG. 1 shows a real-time signalclassification system 100, in accordance with some embodiments of thepresent disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. In some embodiments, components,features, and/or functionality of the system 100 may be similar to thatof vehicle 800 of FIGS. 8A-8D and/or example computing device 900 ofFIG. 9 .

At a high level, the real-time signal classification system 100 mayassign a classification to an input signal in a temporal series. Thesensors 102 may capture a temporal sequence of input signals—such asinput signals representative of video frame—and the preprocessor 106 mayprepare the input signals for the classifier 108. The classifier 108 maybe a multiclass classifier that uses one or more CNNs—or other deepneural network (DNN) and/or machine learning models—to process theinputs. In some embodiments, the classifier 108 may generate twodifferent confidence score distributions where one of the distributionsis generated by a softmax function 114 and the other distribution isgenerated by an angular visual hardness function 112. The classificationmerge component 122 may then combine these two distributions into asingle raw distribution used by subsequent components in the system,such as the class normalization component 124 and/or the classificationchange detector 128.

The class normalization component 124 may normalize the raw distributionusing data from the confusion matrix 126. The confusion matrix 126 mayinclude values representative of a probability that a given inputassigned into a first class, for example, should actually be assigned toa different class. The normalization process can raise or lower a rawconfidence score for a class based on the confusion probabilities withother classes. The normalized confidence score distribution can be sentto the temporal filter 130 for use in making a final classification. Theclassification change detector 128 may determine when a class change hasoccurred within the temporal sequence of input signals and this changedetection may be used to tune the temporal filter 130 in real time tomake a more accurate classification, especially around classtransitions.

The system 100 may include sensors 102 that may generate dimensionaldata (e.g., one-dimensional (1D), 2D, 3D, etc.). For example, one ormore sensors 102 may generate data in a first dimensional space, such as2D, and one or more sensors 102 may generate data in a seconddimensional space, such as 3D. The sensor data may include, withoutlimitation, sensor data from any of the sensors 102 of the vehicle 800(and/or other vehicles or objects, such as robotic devices, VR systems,AR systems, etc., in some examples). For example, and with reference toFIGS. 8A-8C, the sensor data may include the data generated by, withoutlimitation, RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDARsensor(s) 864, stereo camera(s) 868, wide-view camera(s) 870 (e.g.,fisheye cameras), infrared camera(s) 872, surround camera(s) 874 (e.g.,360 degree cameras), long-range and/or mid-range camera(s) 898, and/orother sensor types. For example, although reference is primarily made tothe sensors 102 including cameras and depth sensors (e.g., LIDAR sensors864, RADAR sensors 860, etc.), this is not intended to be limiting, andthe sensors 102 may alternatively or additionally be generated by any ofthe sensors of the vehicle 800, another vehicle, an object, a machine(e.g., a robot), and/or another system (e.g., a virtual vehicle in asimulated environment, a traffic system, a surveillance system, etc.).

In some examples, the sensor data may be generated by one or moreforward-facing sensors, side-view sensors, interior sensors, and/orrear-view sensors of the vehicle 800 and/or other machine type. Thissensor data may be useful for identifying, detecting, classifying,and/or tracking movement of objects around the vehicle 800 and/or othermachines within the environment. In embodiments, any number of sensors102 may be used to incorporate multiple fields of view (e.g., the fieldsof view of the long-range cameras 898, and/or the forward-facing stereocamera 868, and/or the forward facing wide-view camera 870 of FIG. 8B).In some embodiments, such as described herein, signals—e.g.,representing image data—generated by a camera(s) interior to the vehicle800 designed to capture gestures made by a driver, passenger, or otherperson in the vehicle 800 may be processed by one or more DNNs. Theclassification of these signals by the DNNs into a gesture class(es) maybe used to control various components in the vehicle 800, such as acomfort system entertainment system, navigation system, and/or the like.

As such, the inputs to the classifier 108 may include image datarepresenting an image(s) and/or image data representing a video (e.g.,snapshots of video), and/or may represent sensor data generated by asensor depicting a sensory field of the sensor. Where the sensor dataincludes image data, any type of image data format may be used, such as,for example and without limitation, compressed images such as in JointPhotographic Experts Group (JPEG) or Luminance/Chrominance (YUV)formats, compressed images as frames stemming from a compressed videoformat such as H.264/Advanced Video Coding (AVC) or H.265/HighEfficiency Video Coding (HEVC), raw images such as originating from RedClear Blue (RCCB), Red Clear (RCCC), or other type of imaging sensor,and/or other formats. In addition, in some examples, the sensor data maybe used by the system 100 without any pre-processing (e.g., in a raw orcaptured format), while in other examples, the sensor data may undergopre-processing by the sensor data preprocessor 106.

The sensor data preprocessor 106 may perform various operations on thesensor data to generate preprocessed sensor data. Non-limiting examplesof preprocessing operations include noise balancing, demosaicing,scaling, cropping, augmentation, white balancing, tone curve adjustment,and the like. As used herein, the sensor data applied to the classifier108 may reference unprocessed sensor data, pre-processed sensor data, ora combination thereof.

Referring again to FIG. 1 , the outputs of the preprocessor 106 may beapplied to the classifier 108. The classifier 108 may generate rawclassification outputs using the sensor data as input. In someembodiments, the classifier 108 may generate a temporal series of rawclassification outputs for a temporal sequence of sensor data, such as aseries of images. As an example, a temporal series may be a series ofdata points arranged in time order and, in embodiments, the temporalseries of inputs may be a sequence of data captured by the sensors 102at successive equally-spaced points in time (e.g., similar to that of avideo feed). A temporal series of classification distributions maycomprise an individual distribution for each input signal.

The classifier 108 may include a CNN (and/or another type of DNN ormachine learning model), an angular visual hardness function 112, and/ora softmax function 114. Where a CNN is implemented, the CNN can takedifferent forms depending on implementation preferences. For example,the CNN can have different types and combinations of layers (e.g., inputlayers, convolutional layers, pooling layers, ReLU layers,deconvolutional layers, and fully connected layers). In differentembodiments, layers (e.g., convolutional layers) can have differentdimensions that may be selected based on dimensions of an input signal.While described as a CNN herein, embodiments may use other machinelearning models, as described subsequently.

The softmax function 114 may generate a confidence score distributionfrom data generated by the CNN 110. The softmax function 114 maycorrespond to an activation function that turns numbers (e.g., logits)into probabilities that sum to one. The softmax function 114 may outputa vector that represents the probability distributions of a list ofpotential outcomes. This probability distribution may be described as aconfidence score distribution herein. The softmax function 114 may turnlogits (numeric output of the last linear layer of a multi-classclassification CNN 110) into probabilities by taking the exponents ofeach output and then normalizing each number by the sum of thoseexponents so the entire output vector adds up to one—e.g., allprobabilities should add up to one.

The angular visual hardness (AVH) function 112 may also generate aconfidence score distribution from data generated by the CNN 110. AVHmay be computed using the weight vector and the feature map in the lastlayer of the CNN 110. AVH may focus on the angle between these vectorsto generate a confidence score, and the AVH function 112 may generate aconfidence score distribution that assigns a probability to each classthe AVH function 112 (in combination with CNN 110) is trained torecognize.

If the system 100 is for object detection and classification by thevehicle 800, the classes may include, without limitation, vehicles,pedestrians, and animals, or may include more granular classes such asSUVs, sedans, busses, bicyclists, adults, children, dogs, cats, horses,etc. Where the system 100 is for object detection and classification bya robot, the classes may include, without limitation, pedestrians, otherrobots, vehicles, etc. Where the system 100 is for object detection andclassification by an aircraft or drone, the classes may includeaircraft, drones, birds, buildings, vehicles, pedestrians, etc. As such,depending on the implementation of the system 100, the classes that theCNN 110 (and softmax function 114 and AVH function 112) is trained topredict may vary.

As described previously, the softmax function 114 and the AVH function112 may generate confidence score distributions, as illustrated in FIG.2 . FIG. 2 is an illustration of an example softmax output 210 and anexample angular visual hardness output 220, in accordance with someembodiments of the present disclosure. The softmax output 210 may begenerated by the softmax function 114, and the softmax output 210 mayinclude a confidence score for each class the multiclass classifier istrained to identify. In this example, the classes include class A, classB, class C, class D, class B, class F, class G, class H, class I, andclass J. Class J is assigned the highest score of 0.88, while class Areceives the next highest score at 0.07. A score is also assigned to theother classes with the sum of all assigned confidence scores equaling 1.The softmax output 210 may be generated for each image processed in atemporal series of images.

The angular visual hardness output 220 may also include a score for eachclass, and may be generated by the angular visual hardness function 112.Class J is assigned the highest score of 0.92, while class A receivesthe next highest score at 0.05. This illustrates that the AVH output 220and the softmax output 210 may differ. A score is also assigned to theother classes with the sum of all assigned confidence scores equaling 1.The angular visual hardness output 220 may be generated for each imageprocessed of a temporal series of images.

The classification merge component 122 may accept the angular visualhardness output 220 and the softmax output 210 as input and generate asingle confidence score distribution for a single image input into theCNN 110. In some embodiments, the outputs are merged by averaging thetwo outputs (e.g., with equal weighting). In another embodiment, thehighest output assigned to a class in either output is accepted and thelower value dropped. In further embodiments, the lowest output assignedto a class is accepted and the higher value is dropped. In anotherembodiment, more weight is given to one output than the other when theclassification merge component 122 generates the raw confidence scoredistribution for an image. For example, the angular visual hardnessoutput 220 may be given 70% weight in calculating the final combinedconfidence score distribution.

In one embodiment, a comparison is made between one or more classconfidence scores in the two outputs. For example, a comparison of theclass with the highest confidence score in each output may be made and,if the class comparison does not agree (e.g., if the softmax output 210associates a first class with the highest score and the angular visualhardness output 220 associates a second class with the highest score),then the two outputs may not be combined and only one of the outputs maybe used, while the other is dropped. In another embodiment, when thedifference between the highest class confidence score in the softmaxoutput 210 and the highest class confidence score in the angular visualhardness output 220 exceeds a difference threshold, then the higher ofthe two scores may be used without averaging or otherwise combining thetwo outputs. For example, if the softmax class A confidence score is0.91 and the angular visual hardness confidence score for class A is0.73, then only the softmax confidence score would be used if thedifference threshold was 0.15. Otherwise, if the two scores are withinthe threshold, then the two scores are averaged or otherwise combined.The combined confidence score distribution generated by theclassification merge component 122 may be communicated to both the classnormalization component 124 and the classification change detector 128.The combined distribution may be described as the raw confidence scoredistribution or simply the raw distribution.

The classification merge component 122 may correspond to the firstcomponent within the class assignment engine 120. The class assignmentengine 120 may include two parallel processes that may be combined atthe temporal filter 130 to generate a final class assignment for a giveninput signal. One of the two parallel processes may include a classnormalization operation performed by a class normalization component124. Once generated, the normalized confidence score distribution maythen be communicated to the temporal filter 130 for further processing.The second parallel process is a classification change detectionperformed by the classification change detector 128. Classificationchanges may be communicated to the temporal filter 130 and used to tunethe filter in response the detected changes. The change detectionprocess is described with reference to FIG. 3 . The normalizationprocess is described with reference to FIG. 4 . While these processesmay be used together in some embodiments, the two processes may be usedwithout the other in some embodiments. Thus, the normalization processmay work without change detection and the change detection may workwithout normalization.

FIG. 3 illustrates a classification change in response to a signalchange. The classifications of a temporal series of images shown in FIG.3 may be generated by a classifier, such as the classifier 108 describedherein. Example images from a temporal series of images are shown inFIG. 3 . The example images may be captured by a gesture control systemwithin a vehicle, such as vehicle 800. The gesture control systems maycontrol car functions in response to gestures made by a user as capturedin video of a gesture performance area (e.g., a cabin of the vehicle800, or a portion thereof). When no gesture is being made, the imagescaptured should be classified as capturing no control gesture. When theuser makes a gesture within the gesture performance area, the gesturecontrol system may assign a classification to the captured image andperform a corresponding function (e.g., increase the volume). A user maymake a single gesture or a series of gestures. Making a single gesturemay cause the classification system to transition between a no gestureclassification and a classification of the gesture made. Making multiplegestures in series may cause the classification system to transitionbetween different gestures. As such, in some examples, there may be atransition for the classification system to handle.

Transitions may cause uncertainty in classification systems that analyzea series of consecutive input signals to generate an output. As anexample, a classification for a current point in time may be generatedusing the last 20 images captured in a temporal sequence. After atransition occurs, some of the images used to generate an output maycapture an earlier gesture, while another portion of the images capturea current gesture, and a third portion may capture a user transitioningbetween gestures, which is not a gesture at all. The images andcorresponding classifications shown in FIG. 3 illustrate thistransitional challenge.

The example images from the temporal series include a firstfinger-pointing image 310 and a second finger-pointing image 312. Theexample images also include a first v-finger image 314 and a secondv-finger image 316. The first finger-pointing image 310 captures a userpointing an index finger forward. The second finger-pointing image 312also captures a user pointing an index finger forward, but in a positionthat is slightly different from the position captured by the firstfinger-pointing image 310. This difference illustrates the challenge aclassifier faces in classifying an image content. Both images shouldreceive the same classification despite the differences between theimages.

The first v-finger image 314 captures a user pointing two fingersforward forming a V shape. The second v-finger image 316 also captures auser pointing two fingers forward forming a V shape, but in a positionthat is slightly different from the position captured by the firstv-finger image 314. Posing fingers in a V shape is a different gesturethan pointing the index finger forward and should be classified into adifferent class.

The class A graph 320 shows a classification distribution assigned toclass A over the temporal series of images and the class B graph 330shows a classification distribution assigned to class B over thetemporal series of images. In this illustration, class A corresponds tothe finger-pointing gesture and class B corresponds to the V gesture. Ascan be seen, the confidence score that the classifier assigned to classA ranges between one and 0.9 when images 310 and 312 are processed. Theconfidence score drops sharply at transitional entry 322 until itcontinues fluctuating below 0.1 after transitional exit 324. The class Bgraph 330 shows the other side of the transition into class B, where theconfidence increases sharply at transitional entry 232 until itcontinues fluctuating above 0.9 after transitional exit 334.

During a transition represented by entry points 322 and 332 andtransitional exits 324 and 334, the classifier may be analyzing imagesshowing content in two or three different classifications (e.g., classA, class B, and no class). A goal of the technology described herein isto detect these transitions and adapt a temporal filter in real time tomore accurately classify signals received during and after a transition.This improvement may be achieved, in part, using the classificationchange detector 128 in combination with the temporal filter 130.

The classification change detector 128 may analyze the temporal seriesof raw classification distributions to detect a class change. Theclassification change detector 128 may analyze a smaller window ofdistributions than the temporal filter 130. For example, theclassification change detector 128 may look for a change by analyzingsix consecutive distributions, while the temporal filter 130 maygenerate a final classification looking at 20 consecutive distributions.These numbers are simply used for the sake of example and are notintended to be limiting. The classification change detector 128 maydetect a change by looking at the class assigned the highest confidencescore within a distribution. When the class assigned the highestconfidence score changes over a threshold number of consecutivedistributions, then generation of a change notice may be triggered. Thethreshold number may be selected to avoid triggering a change noticeupon detecting a change in just two consecutive distributions, which mayoccur from time to time in response to processing a noisy signal. Adifferent threshold number may be selected for different implementationsdepending on perceived classification jitter (e.g., occurrence of falseclass transitions between consecutive distributions).

In some embodiments, a different threshold number may be used fordifferent class transitions. For example, the confusion matrix 126 mayshow significant class confusion between class A and class B. When twoclasses have a comparatively high amount of class confusion, then alarger threshold number can be used, and when two classes have acomparatively low amount of class confusion, then a lower thresholdnumber can be used. In an embodiment, the classification change detector128 may detect a presumptive class change between two consecutive classdistributions and then determine the threshold to be used based on thetwo classes involved in the change. Once the class-specific threshold ishit, the change notification is generated.

Among other information, the change notification can identify the two ormore classes involved in the change. The change notification can alsoidentify an input signal that corresponds to the transition entry and aninput signal that corresponds to the transition exit. In someembodiments, the transition entry and exit can be used to tune thetemporal filter 130, for example by adjusting an analysis window toexclude class distributions calculated before a transition entry and/orbefore a transition exit. Once generated, the change notification may becommunicated to the temporal filter 130.

As mentioned, the class normalization component 124 may generate anormalized class distribution that adjusts individual confidence scoreswithin the distribution according to a likelihood of confusion betweendifferent classes. The likelihood of confusion is illustrated by theconfusion matrix 126 shown in FIG. 4 .

FIG. 4 shows a confusion matrix 400 for a multiclass classifier trainedto assign an input to one of five different classes. The class confusionmay be determined by analyzing the performance of the trained classifierusing ground truth data. The ground truth data may be determined, as anon-limiting example, by having a user assign a ground truth label tothe signal input used to test the classifier performance. In someembodiments, the class confusion analysis may be an off-line processthat results in a class confusion matrix or other memorialization of theconfusion analysis. However, in other embodiments, the class confusionanalysis may be on an on-line process, a process that occurs atinitialization of the system, and/or at another time.

Data from the confusion matrix 400 may be used in a normalizationprocess. For example, because class confusion may assign a probabilityof occurrence to certain types of classification failures, then, for agiven class, the confusion matrix may include data indicating aprobability that an input signal with a ground truth classification inthe given class is a true positive or a false positive classification. Atrue positive may indicate that the input signal was correctlyclassified into the given class and a false positive may indicate thatan input signal was incorrectly classified into a different class. Eachdifferent class may receive its own probability of receiving a falsepositive classification for the given class.

Each class is assigned both a row and a column in the matrix 400. Inthis example, the multiclass classifier is trained to identify handgestures. The figure point gesture is assigned to column 410 and row420, the finger V gesture is assigned to column 412 and row 422, theflat hand gesture is assigned to column 414 and row 424, the no gestureis assigned to column 416 and row 426, and the thumbs-up gesture isassigned to column 418 and row 428.

The class confusion can be identified by looking at the intersection ofdifferent rows and columns. The lighter the square shading the higherthe confusion. Each square is associated with a probability (not shown).Taking the finger point gesture as an example, the probability assignedto the intersection of column 410 and row 420 may be 94%. This box isthe intersection of the finger point gesture and the finger pointgesture and represents the baseline probability that a finger pointgesture will be correctly identified by the classifier (e.g., truepositive). The probability assigned to the intersection of column 412and row 420 may be 1%, which may indicate there is a 1% probability thatthe finger point gesture will be incorrectly classified as a finger Vgesture. The probability assigned to the intersection of column 414 androw 420 may be 0%, which may indicate that the multiclass classifierdoes not incorrectly assign finger point gestures as flat hand gestures.The probability assigned to the intersection of column 416 and row 420may be 5%, which may indicate there is a 5% probability that the fingerpoint gesture will be incorrectly classified as no gesture. Similarly,the probability assigned to the intersection of column 418 and row 420may be 1%, which may indicate there is a 1% probability that the fingerpoint gesture will be incorrectly classified as no gesture.

The probability that the finger point gesture class is incorrectlyassigned to a different class is recorded in column 410. The probabilityassigned to the intersection of column 410 and row 422 may be 0.13%,which may indicate there is a 0.13% probability that the finger Vgesture will be incorrectly classified as a finger point gesture. Theprobability assigned to the intersection of column 410 and row 424 maybe 0.16%, which may indicate there is a 0.16% probability that the flathand gesture will be incorrectly classified as a finger point gesture.The probability assigned to the intersection of column 410 and row 426may be 13%, which may indicate there is a 13% probability that the flatno gesture will be incorrectly classified as a finger point gesture. Theprobability assigned to the intersection of column 410 and row 428 maybe 17.5%, which may indicate there is a 17.5% probability that thethumbs up gesture will be incorrectly classified as a finger pointgesture. All other boxes in the matrix 400 may also be associated withvalues.

The class normalization component 124 uses the confusion matrix 126 togenerate a normalized class distribution. The normalization process mayadjust the raw classification confidence scores according to data fromthe confusion matrix 400. In general, the confidence score assigned to afirst class (e.g., class A) may be lowered in proportion to theprobability that the first class is a false positive of the otherclasses (e.g., class B, class C, class D). Conversely, the confidencescore for a given class may be increased in proportion to theprobabilities that other classes are false positives for the givenclass. The normalization process may optimize or improve the accuracy ofthe classification by accounting for the probability of different kindsof errors occurring in the classification. As a result, when thenormalization process is combined with the temporal filteringoperation—which uses data from multiple consecutive classifications—theoverall classification accuracy of the system may be meaningfullyimproved without a significant contribution to the overall latency ofthe classification pipeline.

Using the example values described above for the finger point gesture,the normalization of a raw confidence score within a distribution may beillustrated. Assume, as an example, that a raw confidence of 0.9 isassigned to the finger point gesture in the raw distribution. The rawconfidence value may be adjusted upward based on the probabilities thata finger point gesture would be assigned to a different class. Thisprobability can be determined by adding the values in the boxes of row420, excluding the value in the box representing the intersection of row410 and 420, which is a true positive. As described above, these othervalues total to 7%. The raw confidence value may be adjusted downwardbased on the probabilities that a different gesture would be incorrectlyassigned as a finger point gesture. This probability can be determinedby adding the values in the boxes of column 410, excluding the value inthe box representing the intersection of row 410 and 420, which is atrue positive. These other values total 31%. Taken together, the rawconfidence score may be decreased by 24% (+7−31) to 0.684. Other methodsof calculating the normalized confidence score may be used inembodiments of the present disclosure. The overall goal may be toincrease the raw confidence score in proportion to the probability atrue first class input is classified incorrectly into a different classand to reduce the confidence score in proportion to the probability thata first-class classification is assigned when the true class is otherthan the first class. Here the first class is just used as an exampleclass. A similar adjustment can be determined for each class.

The temporal filter 130 may assign a final class distribution using aseries of the normalized class distributions as input. The classassignment engine 120 may use the final distribution to select the finalclass, which may be the class assigned the highest confidence score inthe final distribution. In general, the temporal filter 130 may use atemporal series of class distributions to generate a single classdistribution representative of a classification of a single input. Inthis way, past class distributions contribute to calculating the currentclass distribution.

Depending on the embodiment, the temporal filter may take many differentforms. For example, the temporal filter may be a linear filter, anonlinear filter, an adaptive filter, and/or a statistical filter. Ineach example, the overall operation of the filter may be similar. Forexample, the filter may receive a temporal sequence of outputs from themulticlass classifier 108—e.g., x number of consecutive outputsgenerated by classifying x number of consecutive input signals. Inembodiments, the number of outputs received may be described as ananalysis window. As the outputs are received, the outputs may befiltered together and a final confidence score for each class in eachinstance of the output data may be generated using the temporal filter.

Each individual output in the sequence may include a series ofconfidence scores for each class the multiclass classifier is trained toidentify. For example, a classifier trained to assign one of fivedifferent classes to an input signal would output a confidence score foreach of the five classes. As described herein, the temporal filter mayreceive, as input, a sequence of outputs of the multiclass classifierand generate a final confidence factor for each class. The finalconfidence factor may correspond to the final output of the process andeffectively replace the newest raw output within the sequence of outputsinput to the temporal filter. The final output may then be used toassign an active classification to the corresponding input signal, andthis process may repeat as new outputs are received from theclassifier—with the oldest output dropping out of the sequence and thenewest one being added (e.g., as a rolling buffer of output signals).

As mentioned, the temporal filter may be a linear filter, a nonlinearfilter, an adaptive filter, and/or a statistical filter. Linear filtersprocess time-varying input signals to produce output signals, subject tothe constraint of linearity (i.e., the results can be graphed to form aline). The nonlinear filter may be an exponential filter that smoothstime series data using an exponential window function. Whereas a simplemoving average of the past observations are weighted equally (e.g.,statistical filter), exponential functions are used to assignexponentially decreasing weights over time. The statistical filter cancalculate a moving average or some other statistical measure over awindow of observations.

When an adaptive filter is implemented, the adaptive filter may use achange notification generated by the classification change detector 128.The preliminary signal analysis may be, in embodiments, executed over asmaller output window than is used by the temporal filter. For anon-limiting example, the preliminary signal analysis may be over fiveconsecutive outputs, whereas a default window for the temporal filtermay be twenty or more consecutive outputs. In some embodiments, thepreliminary signal analysis may detect a presumptive class change in theclassifier output, for example, as evidenced by the highest confidencescore in the raw output transitioning from association with a firstclass to a second class. This may indicate a classification shift fromthe first class to the second class.

When the temporal filter 130 responds to a change notification generatedby the classification change detector 128, the temporal filter may bedescribed as an adaptive filter. The adaptive filter may be a linearfilter, a nonlinear filter, and/or a statistical filter. The adaptivefilter may adapt differently depending on the underlying filter beingimplemented. For example, an adaptive filter may adjust the window sizefor a statistical filter or a linear filter. The window size may betemporarily decreased in response to a change notification. The goal ofthe decreased window size may be to emphasize more recent scores in thecalculation. Decreasing the window size has the effect of omitting ordercalculations from the temporal series as the older calculations are morelikely to represent pre-transitional observations that will tend to makethe final determination less accurate. Omitting these observationsimproves the accuracy of the final classification result. For example,decreasing the window size from 20 observations to 10 would cause the 10oldest observations to be omitted from the calculation of the classdistribution.

The original window size may be restored to its original size uponprocessing a threshold number of observations (e.g., normalizedclassification distributions). In one embodiment, the threshold numberis equal to or greater than the window size decrease. For example, ifthe window size is decreased by 10 observations then the window sizecould be increased to the original size after processing 10 consecutiveinput signals within a temporal sequence at the decreased window size.In one embodiment, the increase is a step increase. For example, thewindow may increase in size by an increment of one with each additionalobservation processed until the original size is reached.

In the case of an exponential filter (e.g., nonlinear), the adaptivefilter can maintain the same window size, but effectively deemphasizeolder observations by increasing a decay rate within the exponentialfilter. The increased decay rate gives less weight to older observationsand more weight to newer observations.

Now referring to FIGS. 5-7 , each block of methods 500-700, describedherein, comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The methods may also be embodied ascomputer-usable instructions stored on computer storage media. Themethods may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, methods 500-700are described, by way of example, with respect to the real-time signalclassification system 100 of FIG. 1 . However, these methods mayadditionally or alternatively be executed by any one system, or anycombination of systems, including, but not limited to, those describedherein.

With reference to FIG. 5 , FIG. 5 is a flow diagram showing a method 500for assigning a classification to an input signal, in accordance withsome embodiments of the present disclosure. The method 500, at block502, includes receiving, based at least in part on a multiclassclassifier processing the input signal, a raw classification outputrepresentative of a first raw confidence score, the first raw confidencescore corresponding to a first class. The method 500, at block 504,includes computing a first normalization amount corresponding to thefirst class by using a confusion factor between the first class and asecond class, the confusion factor representative of a probability thatthe multiclass classifier will compute, for a generic input signal knownto correspond to the second class, an output indicating that the genericinput signal corresponds to the first class. The method 500, at block506, includes generating a first normalized confidence scorecorresponding to the first class by adjusting the first raw confidencescore according to the first normalization amount. The method 500, atblock 508, includes applying a temporal filter to the first normalizedconfidence score to generate a final confidence score corresponding tothe first class. The method 500, at block 510, includes determining afinal classification for the input signal based at least in part on thefinal confidence score corresponding to the first class.

Now referring to FIG. 6 , FIG. 6 is a flow diagram showing a method 600for assigning a classification to an input signal, in accordance withsome embodiments of the present disclosure. The method 600, at block602, includes receiving a temporal series of raw classification outputsthat a multiclass classifier generated by processing a temporal sequenceof input signals, each raw classification output including a classconfidence score for each of a plurality of classes the multiclassclassifier is trained to identify. The method 600, at block 604,includes detecting a classification state change within a first set ofthe raw classification outputs indicating a probable classificationchange from a first class to a second class. The method 600, at block606, includes tuning, based at least in part on the classification statechange, an adaptive filter to decrease weight given to older confidencescores corresponding to the first class within a temporal sequence ofconfidence scores corresponding to the first class when calculating afinal confidence score corresponding to the first class. The method 600,at block 610, includes applying the adaptive filter to the temporalsequence of confidence scores corresponding to the first class in asecond set of classification outputs to generate the final confidencescore corresponding to the first class. The method 600, at block 612,includes generating a final classification for the input signal usingthe final confidence score.

With reference to FIG. 7 , FIG. 7 is a flow diagram showing a method 700for assigning a classification to an input signal, in accordance withsome embodiments of the present disclosure. The method 700, at block702, includes receiving a temporal series of raw classification outputsthat a multiclass classifier generated by processing a temporal sequenceof input signals, each raw classification output including a classconfidence score for each of a plurality of classes the multiclassclassifier is trained to identify. The method 700, at block 704,includes detecting a classification state change within a first set ofthe raw classification outputs indicating a probable classificationchange from first class to a second class. The method 700, at block 706,includes tuning, based at least in part on the classification statechange, an adaptive filter to decrease weight given to older confidencescores corresponding to the first class within a temporal sequence ofconfidence scores corresponding to the first class when calculating afinal confidence score corresponding to the first class. The method 700,at block 708, includes computing a first normalization amountcorresponding to the first class using a confusion factor between thefirst class and the second class, the confusion factor representative ofa probability that the multiclass classifier will compute, for a genericinput signal known to correspond to the second class, an outputindicating that the generic input signal corresponds to the first class.The method 700, at block 710, includes generating a first normalizedconfidence score corresponding to the first class by adjusting a firstraw confidence score corresponding to the first class according to thefirst normalization amount. The method 700, at block 712, includesapplying the adaptive filter to a second set of classification outputsthat includes the first normalized confidence score to generate thefinal confidence score corresponding to the first class.

The method 700, at block 714, includes generating a final classificationfor the input signal using the final confidence score corresponding tothe first class as input.

Example Machine Learning Models

Although examples are described herein with respect to using DNNs, andspecifically CNNs, as for example CNN 110, this is not intended to belimiting. For example, and without limitation, the DNNs described hereinmay include any type of machine learning model, such as a machinelearning model(s) using linear regression, logistic regression, decisiontrees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor(Knn), K means clustering, random forest, dimensionality reductionalgorithms, gradient boosting algorithms, neural networks (e.g.,auto-encoders, convolutional, recurrent, perceptrons, Long/Short TermMemory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), and/or other typesof machine learning models.

In addition, in some embodiments, the DNNs described herein may includea convolutional layer structure, including layers such as thosedescribed herein. For example, the DNNs may include a full architectureformulated for the task of generating various outputs—such asclassification confidences. Where a CNN is implemented, one or more ofthe layers may include an input layer. The input layer may hold valuesassociated with the input (e.g., vectors, tensors, etc. corresponding tosensor data, voxelized sensor data, feature vectors, etc.). For example,when the sensor data is an image(s), the input layer may hold valuesrepresentative of the raw pixel values of the image(s) as a volume(e.g., a width, W, a height, H, and color channels, C (e.g., RGB), suchas 32×32×3), and/or a batch size, B (e.g., where batching is used)

One or more layers of the DNNs may include 2D and/or 3D convolutionallayers. The convolutional layers may compute the output of neurons thatare connected to local regions in an input layer (e.g., the inputlayer), each neuron computing a dot product between their weights and asmall region they are connected to in the input volume. A result of aconvolutional layer may be another volume, with one of the dimensionsbased on the number of filters applied (e.g., the width, the height, andthe number of filters, such as 32×32×12, if 12 were the number offilters).

One or more of the layers may include a rectified linear unit (ReLU)layer. The ReLU layer(s) may apply an elementwise activation function,such as the max (0, x), thresholding at zero, for example. The resultingvolume of a ReLU layer may be the same as the volume of the input of theReLU layer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down-sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16×16×12 from the 32×32×12input volume). In some examples, the DNNs may not include any poolinglayers. In such examples, strided convolution layers may be used inplace of pooling layers. In some examples, the feature extractorlayer(s) may include alternating convolutional layers and poolinglayers.

One or more of the layers may include a fully connected layer. Eachneuron in the fully connected layer(s) may be connected to each of theneurons in the previous volume. The fully connected layer may computeclass scores, and the resulting volume may be 1×1×number of classes. Insome example, no fully connected layers may be used by the DNNs as awhole, in an effort to increase processing times and reduce computingresource requirements. In such examples, where no fully connected layersare used, the DNNs may be referred to as a fully convolutional network.

One or more of the layers may, in some examples, include deconvolutionallayer(s). However, the use of the term deconvolutional may be misleadingand is not intended to be limiting. For example, the deconvolutionallayer(s) may alternatively be referred to as transposed convolutionallayers or fractionally strided convolutional layers. The deconvolutionallayer(s) may be used to perform up-sampling on the output of a priorlayer. For example, the deconvolutional layer(s) may be used toup-sample to a spatial resolution that is equal to the spatialresolution of the input vector or tensor of the DNN, or used toup-sample to the input spatial resolution of a next layer.

Although input layers, convolutional layers, pooling layers, ReLUlayers, deconvolutional layers, and fully connected layers are discussedherein with respect to the DNN, this is not intended to be limiting. Forexample, additional or alternative layers may be used, such asnormalization layers, SoftMax layers, and/or other layer types.

Different orders and numbers of the layers of the DNNs may be useddepending on the embodiment. In addition, some of the layers may includeparameters (e.g., weights and/or biases), while others may not, such asthe ReLU layers and pooling layers, for example. In some examples, theparameters may be learned by the DNNs during training. Further, some ofthe layers may include additional hyper-parameters (e.g., learning rate,stride, epochs, kernel size, number of filters, type of pooling forpooling layers, etc.)—such as the convolutional layer(s), thedeconvolutional layer(s), and the pooling layer(s)—while other layersmay not, such as the ReLU layer(s). Various activation functions may beused, including but not limited to, ReLU, leaky ReLU, sigmoid,hyperbolic tangent (tan h), exponential linear unit (ELU), etc. Theparameters, hyper-parameters, and/or activation functions are not to belimited and may differ depending on the embodiment.

Example Autonomous Vehicle

FIG. 8A is an illustration of an example autonomous vehicle 800, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 800 (alternatively referred to herein as the “vehicle800”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,and/or another type of vehicle (e.g., that is unmanned and/or thataccommodates one or more passengers). Autonomous vehicles are generallydescribed in terms of automation levels, defined by the National HighwayTraffic Safety Administration (NHTSA), a division of the US Departmentof Transportation, and the Society of Automotive Engineers (SAE)“Taxonomy and Definitions for Terms Related to Driving AutomationSystems for On-Road Motor Vehicles” (Standard No. J3016-201806,published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep.30, 2016, and previous and future versions of this standard). Thevehicle 800 may be capable of functionality in accordance with one ormore of Level 3-Level 5 of the autonomous driving levels. For example,the vehicle 800 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending on theembodiment.

The vehicle 800 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 800 may include a propulsion system850, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 850 may be connected to a drive train of the vehicle800, which may include a transmission, to enable the propulsion of thevehicle 800. The propulsion system 850 may be controlled in response toreceiving signals from the throttle/accelerator 852.

A steering system 854, which may include a steering wheel, may be usedto steer the vehicle 800 (e.g., along a desired path or route) when thepropulsion system 850 is operating (e.g., when the vehicle is inmotion). The steering system 854 may receive signals from a steeringactuator 856. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 846 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 848 and/or brakesensors.

Controller(s) 836, which may include one or more system on chips (SoCs)804 (FIG. 8C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle800. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 848, to operate thesteering system 854 via one or more steering actuators 856, to operatethe propulsion system 850 via one or more throttle/accelerators 852. Thecontroller(s) 836 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 800. The sensor signals may include video signals. Processingthe signals may include assigning a classification to the video contentusing a multiclass classifier, such as classifier 108. The output fromthe classifier 108 may be adjusted using a temporal filter, such astemporal filter 130. The controller(s) 836 may include a firstcontroller 836 for autonomous driving functions, a second controller 836for functional safety functions, a third controller 836 for artificialintelligence functionality (e.g., computer vision), a fourth controller836 for infotainment functionality, a fifth controller 836 forredundancy in emergency conditions, and/or other controllers. In someexamples, a single controller 836 may handle two or more of the abovefunctionalities, two or more controllers 836 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 836 may provide the signals for controlling one ormore components and/or systems of the vehicle 800 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 858 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDARsensor(s) 864, inertial measurement unit (IMU) sensor(s) 866 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 896, stereo camera(s) 868, wide-view camera(s) 870(e.g., fisheye cameras), infrared camera(s) 872, surround camera(s) 874(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 898,speed sensor(s) 844 (e.g., for measuring the speed of the vehicle 800),vibration sensor(s) 842, steering sensor(s) 840, brake sensor(s) (e.g.,as part of the brake sensor system 846), and/or other sensor types.

One or more of the controller(s) 836 may receive inputs (e.g.,represented by input data) from an instrument cluster 832 of the vehicle800 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 834, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle800. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 822 of FIG. 8C), location data(e.g., the vehicle's 800 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 836,etc. For example, the HMI display 834 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.). Objects may beidentified using a multiclass classifier, such as classifier 108. Theoutput from the classifier 108 may be tuned using a temporal filter,such as temporal filter 130.

The vehicle 800 further includes a network interface 824 which may useone or more wireless antenna(s) 826 and/or modem(s) to communicate overone or more networks. For example, the network interface 824 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 826 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 8B is an example of camera locations and fields of view for theexample autonomous vehicle 800 of FIG. 8A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle800.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 800. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 820 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 800 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 836 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (“LDW”), Autonomous Cruise Control(“ACC”), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 870 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.8B, there may any number of wide-view cameras 870 on the vehicle 800. Inaddition, long-range camera(s) 898 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 898 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 868 may also be included in a front-facingconfiguration. The stereo camera(s) 868 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 868 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 868 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 800 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 874 (e.g., four surround cameras 874 asillustrated in FIG. 8B) may be positioned to on the vehicle 800. Thesurround camera(s) 874 may include wide-view camera(s) 870, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 874 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 800 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 898,stereo camera(s) 868), infrared camera(s) 872, etc.), as describedherein.

FIG. 8C is a block diagram of an example system architecture for theexample autonomous vehicle 800 of FIG. 8A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 800 in FIG.8C are illustrated as being connected via bus 802. The bus 802 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 800 used to aid in control of various features and functionalityof the vehicle 800, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 802 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 802, this is notintended to be limiting. For example, there may be any number of busses802, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses802 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 802 may be used for collisionavoidance functionality and a second bus 802 may be used for actuationcontrol. In any example, each bus 802 may communicate with any of thecomponents of the vehicle 800, and two or more busses 802 maycommunicate with the same components. In some examples, each SoC 804,each controller 836, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle800), and may be connected to a common bus, such the CAN bus.

The vehicle 800 may include one or more controller(s) 836, such as thosedescribed herein with respect to FIG. 8A. The controller(s) 836 may beused for a variety of functions. The controller(s) 836 may be coupled toany of the various other components and systems of the vehicle 800, andmay be used for control of the vehicle 800, artificial intelligence ofthe vehicle 800, infotainment for the vehicle 800, and/or the like. Thecontrollers may include a multiclass classifier, such as classifier 108,and/or use the output of such a classifier. The output from theclassifier 108 may be tuned using a temporal filter, such as temporalfilter 130.

The vehicle 800 may include a system(s) on a chip (SoC) 804. The SoC 804may include CPU(s) 806, GPU(s) 808, processor(s) 810, cache(s) 812,accelerator(s) 814, data store(s) 816, and/or other components andfeatures not illustrated. The SoC(s) 804 may be used to control thevehicle 800 in a variety of platforms and systems. For example, theSoC(s) 804 may be combined in a system (e.g., the system of the vehicle800) with an HD map 822 which may obtain map refreshes and/or updatesvia a network interface 824 from one or more servers (e.g., server(s)878 of FIG. 8D).

The CPU(s) 806 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 806 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 806may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 806 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 806 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)806 to be active at any given time.

The CPU(s) 806 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 806may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 808 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 808 may be programmable and may beefficient for parallel workloads. The GPU(s) 808, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 808 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 808 may include at least eight streamingmicroprocessors. The GPU(s) 808 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 808 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 808 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 808 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 808 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 808 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 808 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 808 to access the CPU(s) 806 page tables directly. Insuch examples, when the GPU(s) 808 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 806. In response, the CPU(s) 806 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 808. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 806 and the GPU(s) 808, thereby simplifying the GPU(s) 808programming and porting of applications to the GPU(s) 808.

In addition, the GPU(s) 808 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 808 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 804 may include any number of cache(s) 812, including thosedescribed herein. For example, the cache(s) 812 may include an L3 cachethat is available to both the CPU(s) 806 and the GPU(s) 808 (e.g., thatis connected both the CPU(s) 806 and the GPU(s) 808). The cache(s) 812may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 804 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 800—such as processingDNNs. In addition, the SoC(s) 804 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 806 and/or GPU(s) 808.

The SoC(s) 804 may include one or more accelerators 814 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 804 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 808 and to off-load some of the tasks of theGPU(s) 808 (e.g., to free up more cycles of the GPU(s) 808 forperforming other tasks). As an example, the accelerator(s) 814 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection). Amulticlass classifier, such as classifier 108, may include a CNN. Theoutput from the classifier 108 may be tuned using a temporal filter,such as temporal filter 130.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 808, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 808 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 808 and/or other accelerator(s) 814.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 806. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 814 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 814. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 804 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 814 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Object detection mayuse a multiclass classifier, such as classifier 108. The output from theclassifier 108 may be tuned using a temporal filter, such as temporalfilter 130. Such a confidence value may be interpreted as a probability,or as providing a relative “weight” of each detection compared to otherdetections. This confidence value enables the system to make furtherdecisions regarding which detections should be considered as truepositive detections rather than false positive detections. For example,the system may set a threshold value for the confidence and consideronly the detections exceeding the threshold value as true positivedetections. In an automatic emergency braking (AEB) system, falsepositive detections would cause the vehicle to automatically performemergency braking, which is obviously undesirable. Therefore, only themost confident detections should be considered as triggers for AEB. TheDLA may run a neural network for regressing the confidence value. Theneural network may take as its input at least some subset of parameters,such as bounding box dimensions, ground plane estimate obtained (e.g.from another subsystem), inertial measurement unit (IMU) sensor 866output that correlates with the vehicle 800 orientation, distance, 3Dlocation estimates of the object obtained from the neural network and/orother sensors (e.g., LIDAR sensor(s) 864 or RADAR sensor(s) 860), amongothers.

The SoC(s) 804 may include data store(s) 816 (e.g., memory). The datastore(s) 816 may be on-chip memory of the SoC(s) 804, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 816 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 812 may comprise L2 or L3 cache(s) 812. Reference to thedata store(s) 816 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 814, as described herein.

The SoC(s) 804 may include one or more processor(s) 810 (e.g., embeddedprocessors). The processor(s) 810 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 804 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 804 thermals and temperature sensors, and/ormanagement of the SoC(s) 804 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 804 may use thering-oscillators to detect temperatures of the CPU(s) 806, GPU(s) 808,and/or accelerator(s) 814. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 804 into a lower powerstate and/or put the vehicle 800 into a chauffeur to safe stop mode(e.g., bring the vehicle 800 to a safe stop).

The processor(s) 810 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 810 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 810 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 810 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 810 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 810 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)870, surround camera(s) 874, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 808 is not required tocontinuously render new surfaces. Even when the GPU(s) 808 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 808 to improve performance and responsiveness.

The SoC(s) 804 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 804 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 804 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 804 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 864, RADAR sensor(s) 860,etc. that may be connected over Ethernet), data from bus 802 (e.g.,speed of vehicle 800, steering wheel position, etc.), data from GNSSsensor(s) 858 (e.g., connected over Ethernet or CAN bus). The SoC(s) 804may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 806 from routine data management tasks.

The SoC(s) 804 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 804 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 814, when combined with the CPU(s) 806, the GPU(s) 808,and the data store(s) 816, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 820) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 808.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 800. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 804 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 896 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 804 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)858. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 862, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 818 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 804 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 818 may include an X86 processor,for example. The CPU(s) 818 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 804, and/or monitoring the statusand health of the controller(s) 836 and/or infotainment SoC 830, forexample.

The vehicle 800 may include a GPU(s) 820 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 804 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 820 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 800.

The vehicle 800 may further include the network interface 824 which mayinclude one or more wireless antennas 826 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 824 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 878 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 800information about vehicles in proximity to the vehicle 800 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 800).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 800.

The network interface 824 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 836 tocommunicate over wireless networks. The network interface 824 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 800 may further include data store(s) 828 which may includeoff-chip (e.g., off the SoC(s) 804) storage. The data store(s) 828 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 800 may further include GNSS sensor(s) 858. The GNSSsensor(s) 858 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 858 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 800 may further include RADAR sensor(s) 860. The RADARsensor(s) 860 may be used by the vehicle 800 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 860 may usethe CAN and/or the bus 802 (e.g., to transmit data generated by theRADAR sensor(s) 860) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 860 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 860 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 860may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 800 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 800 lane.

Mid-range RADAR systems may include, as an example, a range of up to 860m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 850 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 800 may further include ultrasonic sensor(s) 862. Theultrasonic sensor(s) 862, which may be positioned at the front, back,and/or the sides of the vehicle 800, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 862 may be used, and different ultrasonic sensor(s) 862 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 862 may operate at functional safety levels of ASILB.

The vehicle 800 may include LIDAR sensor(s) 864. The LIDAR sensor(s) 864may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 864 maybe functional safety level ASIL B. In some examples, the vehicle 800 mayinclude multiple LIDAR sensors 864 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 864 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 864 may have an advertised rangeof approximately 800 m, with an accuracy of 2 cm-3 cm, and with supportfor a 800 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 864 may be used. In such examples,the LIDAR sensor(s) 864 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 800.The LIDAR sensor(s) 864, in such examples, may provide up to a820-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)864 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 800. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)864 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 866. The IMU sensor(s) 866may be located at a center of the rear axle of the vehicle 800, in someexamples. The IMU sensor(s) 866 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 866 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 866 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 866 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 866 may enable the vehicle 800to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 866. In some examples, the IMU sensor(s) 866 and theGNSS sensor(s) 858 may be combined in a single integrated unit.

The vehicle may include microphone(s) 896 placed in and/or around thevehicle 800. The microphone(s) 896 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 868, wide-view camera(s) 870, infrared camera(s) 872,surround camera(s) 874, long-range and/or mid-range camera(s) 898,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 800. The types of cameras useddepends on the embodiments and requirements for the vehicle 800, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 800. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 8A and FIG. 8B.

The vehicle 800 may further include vibration sensor(s) 842. Thevibration sensor(s) 842 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 842 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 800 may include an ADAS system 838. The ADAS system 838 mayinclude a SoC, in some examples. The ADAS system 838 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 860, LIDAR sensor(s) 864, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 800 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 800 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 824 and/or the wireless antenna(s) 826 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 800), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 800, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 860, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle800 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 800 if the vehicle 800 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)860, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 800 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 860, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 800, the vehicle 800itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 836 or a second controller 836). For example, in someembodiments, the ADAS system 838 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 838may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 804.

In other examples, ADAS system 838 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 838 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 838indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 800 may further include the infotainment SoC 830 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 830 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 800. For example, the infotainment SoC 830 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 834, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 830 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 838,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 830 may include GPU functionality. The infotainmentSoC 830 may communicate over the bus 802 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 800. Insome examples, the infotainment SoC 830 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 836(e.g., the primary and/or backup computers of the vehicle 800) fail. Insuch an example, the infotainment SoC 830 may put the vehicle 800 into achauffeur to safe stop mode, as described herein.

The vehicle 800 may further include an instrument cluster 832 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 832 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 832 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 830 and theinstrument cluster 832. In other words, the instrument cluster 832 maybe included as part of the infotainment SoC 830, or vice versa.

FIG. 8D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 800 of FIG. 8A, inaccordance with some embodiments of the present disclosure. The system876 may include server(s) 878, network(s) 890, and vehicles, includingthe vehicle 800. The server(s) 878 may include a plurality of GPUs884(A)-884(H) (collectively referred to herein as GPUs 884), PCIeswitches 882(A)-882(H) (collectively referred to herein as PCIe switches882), and/or CPUs 880(A)-880(B) (collectively referred to herein as CPUs880). The GPUs 884, the CPUs 880, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 888 developed by NVIDIA and/orPCIe connections 886. In some examples, the GPUs 884 are connected viaNVLink and/or NVSwitch SoC and the GPUs 884 and the PCIe switches 882are connected via PCIe interconnects. Although eight GPUs 884, two CPUs880, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 878 mayinclude any number of GPUs 884, CPUs 880, and/or PCIe switches. Forexample, the server(s) 878 may each include eight, sixteen, thirty-two,and/or more GPUs 884.

The server(s) 878 may receive, over the network(s) 890 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 878 may transmit, over the network(s) 890 and to the vehicles,neural networks 892, updated neural networks 892, and/or map information894, including information regarding traffic and road conditions. Theupdates to the map information 894 may include updates for the HD map822, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 892, the updated neural networks 892, and/or the mapinformation 894 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 878 and/or other servers).

The server(s) 878 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training, selflearning, reinforcement learning, federated learning, transfer learning,feature learning (including principal component and cluster analyses),multi-linear subspace learning, manifold learning, representationlearning (including spare dictionary learning), rule-based machinelearning, anomaly detection, and any variants or combinations therefor.Once the machine learning models are trained, the machine learningmodels may be used by the vehicles (e.g., transmitted to the vehiclesover the network(s) 890, and/or the machine learning models may be usedby the server(s) 878 to remotely monitor the vehicles.

In some examples, the server(s) 878 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 878 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 884, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 878 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 878 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 800. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 800, suchas a sequence of images and/or objects that the vehicle 800 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 800 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 800 is malfunctioning, the server(s) 878 may transmit asignal to the vehicle 800 instructing a fail-safe computer of thevehicle 800 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 878 may include the GPU(s) 884 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 9 is a block diagram of an example computing device(s) 900 suitablefor use in implementing some embodiments of the present disclosure.Computing device 900 may include an interconnect system 902 thatdirectly or indirectly couples the following devices: memory 904, one ormore central processing units (CPUs) 906, one or more graphicsprocessing units (GPUs) 908, a communication interface 910, input/output(I/O) ports 912, input/output components 914, a power supply 916, one ormore presentation components 918 (e.g., display(s)), and one or morelogic units 920.

Although the various blocks of FIG. 9 are shown as connected via theinterconnect system 902 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 918, such as a display device, may be consideredan I/O component 914 (e.g., if the display is a touch screen). Asanother example, the CPUs 906 and/or GPUs 908 may include memory (e.g.,the memory 904 may be representative of a storage device in addition tothe memory of the GPUs 908, the CPUs 906, and/or other components). Inother words, the computing device of FIG. 9 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.9 .

The interconnect system 902 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 902 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 906 may be directly connectedto the memory 904. Further, the CPU 906 may be directly connected to theGPU 908. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 902 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 900.

The memory 904 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 900. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 904 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device900. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 906 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 900 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 906 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 906 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 900 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 900, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 900 mayinclude one or more CPUs 906 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 906, the GPU(s) 908 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device900 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 908 may be an integrated GPU (e.g.,with one or more of the CPU(s) 906 and/or one or more of the GPU(s) 908may be a discrete GPU. In embodiments, one or more of the GPU(s) 908 maybe a coprocessor of one or more of the CPU(s) 906. The GPU(s) 908 may beused by the computing device 900 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 908 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 908may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 908may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 906 received via ahost interface). The GPU(s) 908 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory904. The GPU(s) 908 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 908 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 906 and/or the GPU(s)908, the logic unit(s) 920 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 900 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 906, the GPU(s)908, and/or the logic unit(s) 920 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 920 may be part of and/or integrated in one ormore of the CPU(s) 906 and/or the GPU(s) 908 and/or one or more of thelogic units 920 may be discrete components or otherwise external to theCPU(s) 906 and/or the GPU(s) 908. In embodiments, one or more of thelogic units 920 may be a coprocessor of one or more of the CPU(s) 906and/or one or more of the GPU(s) 908.

Examples of the logic unit(s) 920 include one or more processing coresand/or components thereof, such as Tensor Cores (TCs), Tensor ProcessingUnits(TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs),Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs),Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), ArtificialIntelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs),Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits(ASICs), Floating Point Units (FPUs), input/output (I/O) elements,peripheral component interconnect (PCI) or peripheral componentinterconnect express (PCIe) elements, and/or the like.

The communication interface 910 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 900to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 910 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet.

The I/O ports 912 may enable the computing device 900 to be logicallycoupled to other devices including the I/O components 914, thepresentation component(s) 918, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 900.Illustrative I/O components 914 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 914 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 900. Thecomputing device 900 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 900 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 900 to render immersive augmented reality or virtual reality.

The power supply 916 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 916 may providepower to the computing device 900 to enable the components of thecomputing device 900 to operate.

The presentation component(s) 918 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 918 may receivedata from other components (e.g., the GPU(s) 908, the CPU(s) 906, etc.),and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-usable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method for assigning a classification to aninput signal comprising: computing, using a neural network, a firstconfidence score for a first input signal, the first confidence scorecorresponding to a first class represented in the first input signal;computing, using a confusion factor between the first class and a secondclass, a first normalization amount corresponding to the first class,the confusion factor representative of a probability that the neuralnetwork will compute, for a second input signal associated with thesecond class, an output indicating that the second input signalcorresponds to the first class; generating a first normalized confidencescore corresponding to the first class by adjusting the first confidencescore according to the first normalization amount calculated using theconfusion factor; applying a filter to the first normalized confidencescore to generate a final confidence score corresponding to the firstclass; and determining a final classification for the first input signalusing the final confidence score corresponding to the first class. 2.The method of claim 1, wherein the neural network is part of a gesturecontrol system.
 3. The method of claim 1, wherein the neural network iscomprised in at least one of: a control system for an autonomous orsemi-autonomous machine; a perception system for the autonomous orsemi-autonomous machine; a system for performing simulation operations;a system for performing deep learning operations; a system implementedusing an edge device; a system implemented using a robot; a gamingsystem; a system incorporating one or more virtual machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.
 4. Themethod of claim 1, wherein the adjusting the first confidence scoreaccording to the first normalization amount includes subtracting thefirst normalization amount from the first confidence score.
 5. Themethod of claim 1, wherein the filter includes at least one of atemporal filter, an adaptive filter, or a temporal adaptive filter. 6.The method of claim 1, wherein the first confidence score is generatedby combining an output of a softmax calculation with an output of anangular visual hardness calculation.
 7. A method for assigning aclassification to an input signal comprising: receiving a temporalseries of one or more classification outputs generated by processing atemporal sequence of input signals using a neural network; detecting aclassification state change within the one or more classificationoutputs indicating a probable classification change from a first classto a second class; tuning, based at least in part on the classificationstate change, a filter to adjust a parameter corresponding to one ormore first outputs in the one or more classification outputs; applyingthe filter to the one or more first outputs in the one or moreclassification outputs to generate a final confidence score; andgenerating a final classification for the input signal using the finalconfidence score.
 8. The method of claim 7, wherein the parameter is aweight given to the one or more classification outputs when generatingthe final confidence score.
 9. The method of claim 8, wherein theparameter is adjusted to give less weight when generating the finalconfidence score to at least one classification output of the one ormore classification outputs that was generated before at least one otherclassification output of the one or more classification outputs whengenerating the final confidence score.
 10. The method of claim 9,wherein the weight is decreased by increasing a rate of decay in a decayfunction within the filter.
 11. The method of claim 9, wherein theweight is decreased by using fewer than a default amount of one or moreclassification outputs when generating the final confidence score. 12.The method of claim 7, further comprising: computing, using a confusionfactor between the first class and the second class, a firstnormalization amount corresponding to the first class, the confusionfactor representative of a probability that the neural network willcompute, for a second input signal associated with the second class, anoutput indicating that the second input signal corresponds to the firstclass; generating a first normalized confidence score corresponding tothe first class by adjusting a first confidence score according to thefirst normalization amount calculated using the confusion factor; andusing the first normalized confidence score to generate the finalclassification.
 13. The method of claim 12, wherein the generating thefirst normalized confidence score includes subtracting the firstnormalization amount from the first confidence score.
 14. The method ofclaim 7, wherein the temporal sequence of input signals is generated byan autonomous or semi-autonomous machine.
 15. The method of claim 7,wherein the neural network is comprised in at least one of: a controlsystem for an autonomous or semi-autonomous machine; a perception systemfor the autonomous or semi-autonomous machine; a system for performingsimulation operations; a system for performing deep learning operations;a system implemented using an edge device; a system implemented using arobot; a gaming system; a system incorporating one or more virtualmachines (VMs); a system implemented at least partially in a datacenter; or a system implemented at least partially using cloud computingresources.
 16. A method for assigning a classification to an inputsignal comprising: receiving a temporal series of one or moreclassification outputs generated by processing a temporal sequence ofinput signals using a neural network; detecting a classification statechange within the one or more classification outputs indicating aprobable classification change from a first class to a second class;tuning, based at least in part on the classification state change, afilter to adjust a parameter associated with one or more older outputsin the one or more classification outputs; computing a firstnormalization amount corresponding to the first class using a confusionfactor between the first class and the second class, the confusionfactor representative of a probability that the neural network willcompute, for a generic input signal associated with the second class, anoutput indicating that the generic input signal corresponds to the firstclass; and generating a final classification for the input signal usingthe filter and the first normalization amount.
 17. The method of claim16, wherein the neural network is part of a gesture control system. 18.The method of claim 16, wherein the neural network is comprised in atleast one of: a control system for an autonomous or semi-autonomousmachine; a perception system for the autonomous or semi-autonomousmachine; a system for performing simulation operations; a system forperforming deep learning operations; a system implemented using an edgedevice; a gaming system; a system implemented using a robot; a systemincorporating one or more virtual machines (VMs); a system implementedat least partially in a data center; or a system implemented at leastpartially using cloud computing resources.
 19. The method of claim 16,wherein the generating the final classification for the input signalusing the first normalization amount includes subtracting the firstnormalization amount from a first confidence score.
 20. The method ofclaim 16, wherein the generating the final classification for the inputsignal using the filter includes decreasing an amount of classificationoutputs of the one or more classification outputs used to generate thefinal classification from a default amount.